Abstract

Cancer is a highly heterogeneous disease, so much so that no two tumors are identical. Consequently, the responses of different tumors to a given therapy are also highly heterogeneous, even between tumors that grow in the same organ. Developing tools that enable prediction of tumor response and aid selection of the most suitable treatment strategies is therefore critical to substantially increase the survival of cancer patients. In a recent study, Tiriac et al. demonstrate the utility of pancreatic ductal adenocarcinoma (PDAC) patient–derived organoids (PDOs) to identify novel driver genes, predict therapeutic responses, and potentially design personalized treatments for cancer patients.

To test the potential of PDOs as treatment prediction tools, the authors efficiently generated a library of PDAC PDOs from surgical resections and fine needle biopsies. Genomic and transcriptional profiling revealed that the majority of PDOs represented the most frequent genetic alterations and transcriptional subtypes present in PDAC patients. Of note, whole-genome sequencing of a subset of PDOs identified novel genetic alterations and chromosomal rearrangements that were undetectable in their matched primary tumors due to the low cellularity of PDAC specimens. Therapeutic profiling or “pharmacotyping” of PDAC PDOs with five chemotherapeutic agents routinely used in PDAC patients indicated that PDOs recapitulated patient outcomes for these treatments. Pharmacotyping of PDOs was also able to capture tumor evolution and intrapatient heterogeneity and was used to create transcriptional signatures that might predict response of PDAC patients to the chemotherapeutics. Last, pharmacotyping of PDAC PDOs with a panel of targeted agents identified alternative targeted therapies for chemotherapy-resistant PDOs that could potentially be successful in PDAC patients.

This study highlights that combining genomic and transcriptional profiling of PDOs with pharmacotyping could lead to the design of efficient personalized treatments. Although additional work is required to further improve the efficiency of PDO establishment and the predictive power of the treatment response signatures and novel treatment nomination, this study proposes a quick pipeline to exploit PDOs for cancer precision medicine. This first step is already promising.